Recommendation has become a common feature in many electronic devices and other systems. When there are a multitude of choices available to a user or when a user is unaware of the availability of potentially desirable goods and services, a recommendation system can be a useful tool for the user by bringing relevant choices to the attention of the user. Thus, a lot of time and effort can be saved by the user in discovering individual choices.
Recommendation systems are commonly used by online retailers to generate purchase recommendations. Such recommendation systems are also used by media content providers to generate, for example, movie, music and news content recommendations. The recommendations are typically based on the user's own past behaviours and other similar users' past behaviours. Various degrees of personalisation may also be employed by a recommendation system to relate a recommendation to a personal aspect of the user in order to increase the effectiveness of the recommendation. One example of such personalisation is the making of a recommendation for purchasing a gift for a loved one near a birthday of the loved one.
As the popularity of using various recommendation methods grows, the number of recommendations targeting a user has also increased dramatically. A user may receive a recommendation on a number of devices available to the user, such as a mobile phone (e.g, a smart phone), tablet or personal computer (PC) in various forms such as an email, a notification on a mobile application or an advertisement on a web page. Often, these recommendations are presented to the user unsolicited, causing the user to perceive them as advertising spam, especially when the current environment or user's mind-set is unsuitable to act upon a recommendation. Under such circumstances, a recommendation presented to the user may be ignored or, at best, the action required for the recommendation may be postponed. Any postponed recommendations may be forgotten by the user or the desire for utilising the recommendations may wane before the user has a chance to follow-up on the recommended action. Such postponed recommendations can cause a low utilisation of recommendations even if the recommendations are relevant and appropriately personalised.